Capstone Project

Pneumonia Detection

Problem Statement: Chest Radiograph is the most commonly used or performed diagnostic imaging Technology. Due to high volume of chest radiography, it could be very time consuming and intensive for the radiologists to review each image manually. As such, an automated solution is ideal to locate the position of inflammation in an image. By having such an automated pneumonia screening system, this can assist physicians to make better clinical decisions.

Business Domain Value: Automating Pneumonia screening in chest radiographs, providing affected area details through bounding box. Assist physicians to make better clinical decisions or even replace human judgement in certain functional areas of healthcare (eg, radiology). Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.

Details about the data and dataset files are given in below link, https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data

Purpose of this project: Find out a patient with pnuemonia disease. There are some patients, which have some symptoms but not sure they might get affected by this diesea or not. This project also help to find out those patients, who have some symptoms and might get affected by this disease.

Database table Preprocessing

Observation : No missing value found in the class_info table. All the target values are filled therefore there is no empty value, cell with NaN or empty cell in label_data are having no phenumonia so let's replace those value with 0.

There are 3 classes -

  1. Lung Opacity
  2. No Lung Opacity / Not Normal
  3. Normal

No Lung Opacity / Not Normal has been classified as Target 0

Normal has been classified as 0 as well

Lung Opacity has been classified as 1

Data Visualisation

VGG-16

Without a Pre-Trained Model

Model testing

END